Aparna Dhinakaran of Arize AI argues that the AI engineer's job is shifting from writing features to building the loop that builds them: evals that review non-deterministic work, telemetry that agents consume, and always-on agents that act on it. She uses Arize's Signal agent as a case study, covering triggers, sandboxes, durable state, model and harness independence, and evaluating the evaluators.
Alex Lieberman and CJ Hess of Tenex discuss how an AI-native engineering consultancy organizes its work around outcomes, not hours. They cover story-point compensation, the MetaHarness workflow that keeps plans and decisions in Git, token economics and model choices, the gap between enterprise AI rollouts and production agents, why vertical AI and mid-tier SaaS may or may not survive, and what this means for jobs and New York's AI scene.
Eugene Cheah, CEO of Featherless AI, presents a four-step framework for cutting AI inference costs at scale: measure token spend by team and workload, right-size models through evals, classifiers, and fine-tuning, buy GPU capacity with an understanding of the speed-versus-cost tradeoff, and check whether organizational processes can absorb the extra output.
Dru Knox of Tessl discusses harness engineering, the discipline for building software factories with AI agents. He outlines a factory's three pillars—autonomy, automation, quality—and introduces inner, outer, and meta loops. Knox explains why this field is challenging and proposes a three-layer architecture: control plane, agent-ready stack, and improvement loops, concluding with key metrics to track progress towards an agent-driven software development future.
This presentation by Jeff Koss and Leo Platzer from Deasy Labs (now Collibra) tackles the critical challenge of scaling AI chatbots from pilot projects to production with large, messy unstructured datasets. They demonstrate a solution for curating and cleaning 80,000+ SharePoint documents, highlighting the importance of data quality—specifically identifying duplicates, conflicts, sensitive information, and ensuring data freshness—to significantly improve RAG chatbot performance and reduce development time.
The AI Engineer Paris 2026 main stage featured discussions on the historical impact of AI as a general-purpose technology, the evolving landscape of AI engineering towards sophisticated "software factories," and Mistral AI's strategic vision for building frontier AI, ensuring agentic safety, and addressing data sovereignty through advanced models and infrastructure.